Search Results (1,098)

Most studies on countries’ innovation focus on its overall assessment, neglecting the interactions of its components. This article discusses the EU-27 countries’ innovation in each of its pillars, Framework conditions, Investments, Innovation activities, and Impacts, as defined in the European Innovation Scoreboard 2025. We quantitatively examine the connections among the innovation pillars and compare the results of the synthetic measure of innovation indicator with the SDG Index. First, we use the zero-unitarisation method to calculate four synthetic measures of countries’ innovation. Then, we perform canonical correlation analysis to examine the interconnections among the measures. Subsequently, we propose rankings and classifications of countries based on their innovation levels. The results show that, although the four pillars of innovation are interrelated, Framework conditions are of key importance, with their impact being most evident in relation to Impacts. Sweden, Finland, and Denmark were the leaders in pillars of innovation and sustainable development. However, we found that some countries (Poland, Slovakia, and Latvia) with lower innovation levels still had higher SDG Index values, placing them in the more sustainable group. The results of the study show that the relationship between innovation and sustainable development is not simple or linear. There are EU-27 countries that rank highly in one area but not the other. The results not only allowed for the assessment of the EU-27 countries in terms of innovation but also indicated precise relationships within this framework, linking innovations with sustainable development. Full article

Universities are central to regional high-quality development, yet existing studies often rely on output-based indicators and neglect efficiency as well as the contributions of architecture and engineering disciplines. This study addresses this limitation by constructing an evaluation–identification framework that links technological innovation efficiency to regional development. Regional progress is measured with a composite index derived from multi-criteria decision analysis; innovation efficiency is evaluated using a non-oriented DEA–SBM model under constant returns to scale; and causal effects are tested with a two-way fixed-effects panel approach. Results reveal steady growth in regional development, marked spatial disparities in efficiency, with frontiers concentrated in certain provinces, and a consistently positive effect of efficiency on development, with stronger marginal impacts in central and western regions. By adopting an efficiency–mechanism perspective, the study highlights architecture-related disciplines as key drivers of sustainable growth and provides guidance for innovation alliances, evaluation reform, and managerial enhancement. Full article

To address the challenges in simulating nonpoint source pollution inflow, pollutant source distribution, and migration pathways in plain river network regions, this study innovatively proposes an optimized technical framework based on the NDR module of the InVEST model. Through land use data reconstruction, DEM negative value correction, and flow accumulation threshold optimization, the framework effectively resolves key issues including pollutant receiving water identification, runoff path simulation, and pollutant migration termination determination, significantly enhancing the model’s applicability to complex river systems. Using the Taipu River as a case study, this research investigates the spatial distribution characteristics of nonpoint source pollution load inflow and its sources in major rivers within plain river network regions. Results show that in 2023, total nitrogen and total phosphorus inflows into the Taipu River were 1004.11 t/a and 83.80 t/a, respectively, with pollution loads primarily originating from the Wangning Polders in the midstream, Chengnan New District Small Watersheds and Chang Yang River Small Watersheds, mainly entering the Taipu River through tributaries such as the Beijing-Hangzhou Grand Canal and Nanzha Port. Calculations based on measured data indicate total nitrogen and total phosphorus inflows into the Taipu River were approximately 1300 t/a and 90 t/a, respectively, consistent with model predictions. Building on environmental capacity assessment results, this study proposes targeted recommendations for precision-based nonpoint source pollution control in the Taipu River basin. The findings provide scientific evidence and technical paradigms for nonpoint source pollution management and sustainable management in plain river network regions. Full article

Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant. Full article

This study investigates territorial disparities in transition toward circular economy within Italy, introducing an innovative methodological approach aimed at measuring regional efficiency and inequality. The research develops two complementary analytical tools: the Regional Circular Economy Index (ReCEI), a composite indicator designed for comparative evaluation of circular economy performance across regions, and the Regional Circular Economy Disparity Index (ReCED), inspired by the model of Sen which quantifies both the magnitude and spatial distribution of territorial inequalities. Applying this integrated framework to the 20 Italian regions reveals a pronounced heterogeneity: a select group of regions achieves or approaches efficiency benchmarks, whereas others exhibit persistent structural delays linked to infrastructural, institutional and innovative deficits. These findings, thus, confirm the persistence of a territorial dualism in the circular transition, only partially mitigated by instances of advanced governance and coordinated policies. Full article

Urban Green Spaces (UGSs) are essential for ecological sustainability and public health, offering benefits such as air pollution reduction, urban cooling, and recreational opportunities. However, existing evaluation tools remain inconsistent, often assessing isolated dimensions like accessibility or aesthetics without fully integrating health considerations. A systematic approach is needed to understand how these tools measure UGS quality and their relevance to health outcomes. This study employs a literature review (PRISMA framework) to analyze UGS evaluation tools with a focus on quality and health implications. A search in Scopus and Web of Science identified 14 relevant studies. Data extraction examined tool structure, assessed dimensions, data collection methods, geographic applications, and integration of health indicators. The review identified 13 distinct tools varying in complexity and methodology, from standardized checklists to GIS-based analyses. While key dimensions included accessibility, safety, aesthetics, and biodiversity, health-related factors were inconsistently integrated. Few tools explicitly assessed physical, mental, or social health outcomes. Technological innovations, such as Google Street View and AI-based analysis, emerged as enhancements for UGS evaluation. Despite methodological advances, gaps remain in linking UGS quality assessments to health outcomes. The lack of standardized health metrics limits applicability in urban planning. Future research should focus on interdisciplinary frameworks integrating environmental and health indicators to support the creation of sustainable and health-promoting UGS. Full article

The rising presence of artificial intelligence (AI) across European industries is gradually reshaping how societies manage resources, reduce waste, and pursue long-term sustainability. While researchers widely acknowledge the economic and social implications of AI, they have not yet sufficiently explored its contribution to advancing a circular economy. This study examines how varying levels of AI adoption across EU Member States relate to material footprint, resource productivity, waste generation, and recycling performance. The analysis draws on harmonized Eurostat data from 2023, the most recent year for which complete and comparable indicators are available, enabling a coherent cross-sectional perspective that reflects the period when AI began to exert a more visible influence on economic and environmental practices. By combining measures of AI uptake with key circular economy indicators and applying factor analysis, neural network modelling, and cluster analysis, the study identifies underlying patterns and country-specific profiles. The results suggest that higher AI adoption is often associated with greater resource productivity and more efficient material use. However, its effects on waste generation and recycling remain uneven across Member States. These findings indicate that AI can support circular economy objectives when embedded in coordinated national strategies and supported by robust institutional frameworks. Strengthening the alignment between digital innovation and sustainability goals may help build more resilient, resource-efficient economies across Europe. Full article

This study investigates the relationship between architectural colour and cultural identity in the ancient villages of Hebei Province, emphasising the role of colour in cultural heritage preservation and sustainable development. The research aims to (1) identify the dominant chromatic attributes of Hebei’s village architecture, (2) interpret their cultural and symbolic meanings, and (3) construct a colour model applicable to heritage conservation. A qualitative case study approach was employed across four representative villages, combining field surveys, semi-structured interviews, and digital colour analysis using the COLORO system. The findings reveal that the prevailing hues—ranging from red and yellow to ochre and brown—derive from local stone and timber, embodying values of stability, humility, and harmony with the environment. Decorative elements in bright red and gold signify celebration and community vitality, contrasting with the subdued architectural tones. Integrating these empirical and cultural insights, this study proposes the Colour Symbol System for Hebei Ancient Villages (CSSHAV)—a model that unites quantitative colour parameters with qualitative cultural interpretation. The CSSHAV serves as a practical framework for guiding colour conservation, policy development, and sustainable design in rural heritage contexts. The originality of this study lies in bridging scientific colour measurement with cultural semiotics, providing both theoretical advancement and actionable guidance for the preservation of regional chromatic identity. The findings identify increasing risks of colour homogenisation under the pressures of globalisation. Through the CSSHAV model, it proposes strategies to preserve Hebei’s traditional chromatic identity by integrating digital colour analysis with cultural interpretation. This balance between conservation and innovation contributes to sustaining both the aesthetic integrity and cultural vitality of ancient villages. Full article

An improved understanding of the sanitation enabling environment and status of market development (“sanitation economy”) is crucial not only for advancing national and global sanitation goals, but also for attracting the financing necessary to drive meaningful progress in low- and middle-income countries (LMICs). This need is particularly pressing as the sanitation sector faces a significant funding gap that must be bridged to meet the growing demands for sanitation services, infrastructure, and innovation. This paper reviews frameworks that assess the sanitation economy in LMICs with the aim of informing the development of more impactful future frameworks and the wider application of existing frameworks. Frameworks were identified through internet search and interviews with representatives of international sanitation sector organisations and universities. Thirty-nine frameworks were identified that have been or are currently being used in sanitation. Frameworks are diverse in the structure they adopt, their focus areas, the number of indicators, the number of countries covered, the frequency with which they have been applied, their reliance on primary versus secondary data sources, and their uptake and impact. Overall, use of the frameworks has been piecemeal and sporadic in LMICs. Only few frameworks have been picked up and applied by another organisation, although the results of some frameworks are widely used and cited. To ensure future efforts to measure and monitor the sanitation economy are evidence-based and make the best use of limited resources, frameworks currently in use should be independently evaluated and there should be greater collaboration and adoption of common frameworks. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) enables the large-scale collection of ecological data, yet translating this dynamic sensor data into engaging, personalized public experiences remains a significant challenge. Existing solutions fall short: static exhibitions lack adaptability, while general-purpose LLM agents struggle with real-time responsiveness and reliability. To address this, we introduce CurationAgent, a novel intelligent agent built upon the State-Gated Agent Architecture (SGAA). Its core innovation is an advanced hybrid curation pipeline that synergizes Retrieval-Augmented Generation (RAG) for broad semantic recall with an Intent-Driven Curation (IDC) Funnel for precise intent formalization and narrative synthesis. This hybrid model robustly translates user intent into a curated, multi-modal narrative. We validate this framework in a proof-of-concept virtual exhibition of the Lalu Wetland’s biodiversity. Our comprehensive evaluation demonstrates that CurationAgent is significantly more responsive (1512 ms vs. 4301 ms), reliable (95% vs. 57% task success), and precise (85.5% vs. 52.7% query precision) than standard agent architectures. Furthermore, a user study with 27 participants confirmed our system leads to measurably higher user engagement. This work contributes a robust and responsive agent architecture that validates a new paradigm for interactive systems, shifting from passive information retrieval to active, partnered experience curation. Full article

Background/Objectives: Acute stress is known to influence food-related motivation and decision-making, often promoting a preference for energy-dense, palatable foods. However, traditional laboratory paradigms have limited ecological validity. This study examined the relationship between stress-induced physiological changes, eating behavior traits, and food cravings using a virtual reality (VR) adaptation of the Trier Social Stress Test (VR-TSST) followed by a VR supermarket task in adolescents. Methods: Thirty-eight adolescents (mean age 15.8 ± 0.6 years) participated in the study. Physiological parameters (HR, QT, PQ intervals) were recorded pre- and post-stress using a portable ECG device (WIWE). Perceived stress and eating behavior traits were evaluated with the Perceived Stress Scale (PSS) and the Three-Factor Eating Questionnaire (TFEQ-R21C), respectively. Immediately after the VR-TSST, participants performed a VR supermarket task in which they rated cravings for sweet, fatty, and healthy foods using visual analog scales (VAS). Paired-samples t-tests examined pre–post changes in physiological parameters, partial correlations explored associations between ECG responses and eating traits, and a 2 × 3 mixed-model Repeated Measures ANOVA assessed the effects of food type (sweet, fatty, healthy) and uncontrolled eating (UE) group (low vs. high) on post-stress cravings. Results: Acute stress induced significant increases in HR and QTc intervals (p < 0.01), confirming a robust physiological stress response. The ANOVA revealed a strong main effect of food type (F(1.93, 435.41) = 168.98, p < 0.001, η²p = 0.43), indicating that stress-induced cravings differed across food categories, with sweet foods rated highest. A significant food type × UE group interaction (F(1.93, 435.41) = 16.49, p < 0.001, η²p = 0.07) showed that adolescents with high UE exhibited greater cravings for sweet and fatty foods than those with low UE. Overall, craving levels did not differ significantly between groups. Conclusions: The findings demonstrate that acute stress selectively enhances cravings for high-reward foods, and that this effect is modulated by baseline uncontrolled eating tendencies. The combined use of VR-based stress induction and VR supermarket simulation offers an innovative, ecologically valid framework for studying stress-related eating behavior in adolescents, with potential implications for personalized nutrition and the prevention of stress-induced overeating. Full article

China has shifted from a stage of rapid growth to a stage of high-quality development. This highlights the critical need for policy frameworks that synergistically align technological innovation with sustainable development. To address the research gap in systematically assessing the collaboration between New Energy Industry (NEI) policies and New Quality Productive Forces (NQPF), this study proposes a three-dimensional “Goal-Instrument-Value” framework. Methodologically, we employ a combination of deep learning models (including LDA topic modeling, LSTM networks, and the Soft EDA algorithm) and policy quantification methods, analyzing 135 NQPF policies and nearly 800 NEI policies. The findings reveal a significant and strengthening synergy between the two policy domains. Notably, a misalignment exists in the goal dimension, where the weight of science and technology in NEI policies remains modest at 20%, indicating substantial potential for enhancement. In the instrument dimension, there is a predominant reliance on economically driven instruments, along with a notable underutilization of environmental instruments. Nevertheless, the overall synergy in policy value, as measured by a specialized New-Force Dictionary and the BM25 model, exhibits a consistent upward trend. Based on these findings, we recommend strengthening investment in NEI technology R&D, increasing the deployment of environment-oriented policy instruments, and establishing a cross-departmental policy synergy mechanism. These measures are crucial to fully harness the synergistic potential of NEI and NQPF policies for accelerating China’s green industrial transformation and achieving its sustainable development goals. Full article

Three-dimensional printed food has rapidly positioned itself at the intersection of food technology and personalized nutrition, opening up new perspectives for sustainable production, creative customization, and more efficient resource use. Although global interest in this innovation continues to grow, consumer acceptance remains largely underexplored in Central and Eastern Europe. This study analyzes how Romanian consumers approach the adoption of 3D-printed food by applying an extended UTAUT2 framework to a sample of 608 urban respondents. Using structural equation modeling, it examines the influence of expected effort, performance expectancy, social influence, and perceived compatibility on adoption intention, while price perception is introduced as a key mediating variable—a novel and meaningful contribution to the literature on food technology acceptance. Given the non-probabilistic sampling design, the difficulties encountered in measuring Hedonic Motivation and Facilitating Conditions, and the early diffusion stage of 3D food printing in Romania, the present work should be viewed as a robust exploratory investigation based on Structural Equation Modeling (SEM) among urban Romanian consumers, providing first empirical evidence on 3D-printed food acceptance in Eastern Europe rather than definitive conclusions for the entire population. The results highlight that utilitarian and social factors are decisive: expected effort enhances perceived performance, while performance, social influence, and compatibility significantly strengthen perceptions of price fairness. In turn, price perception strongly predicts consumers’ behavioral intention to adopt 3D-printed food. Hedonic motivation and facilitating conditions were not statistically significant and were therefore removed from the final model. These findings show that, in emerging food markets, consumers tend to make adoption decisions based more on rational value assessments than on novelty or convenience. The study contributes to theory by embedding price perception into the UTAUT2 framework and to practice by identifying the key elements that can boost market readiness—transparent pricing and closer alignment with consumer values. By filling an important gap in the empirical literature from Eastern Europe and focusing on price as a cognitive bridge between technological and psychological drivers, this paper offers a timely and relevant contribution to ongoing research on consumer perception and acceptance of food innovations. For Eastern European food innovation research, this study provides one of the first quantitative analyses of 3D-printed food acceptance that explicitly links technology-related beliefs to price perception in a regional, price-sensitive context. Full article

Background/Objectives: Constructing a comprehensive spatiotemporal map of tumor heterogeneity is essential for understanding tumor evolution, with copy number variation (CNV) being a significant feature. Existing studies often rely on tools originally developed for single-cell data, which fail to utilize spatial information, often leading to an incomplete map of clonal architecture. Our study aims to develop a model that fully leverages spatial omics data to elucidate spatio-temporal changes in tumor evolution. Methods: Here, we introduce SCOIGET (Spatial COpy number Inference by Graph on Evolution of Tumor), a novel framework using graph neural networks with graph attention layers to learn spatial neighborhood features of gene expression and infer copy number variations. This approach integrates spatial multi-omics features to create a comprehensive spatial map of tumor heterogeneity. Results: Notably, SCOIGET achieves a substantial reduction in error metrics (e.g., mean squared error, cosine similarity, and distance measures) and produces superior clustering performance, as indicated by higher Silhouette Scores compared to existing methods, validated by both simulated data with spot-level ground truth and patient cohorts. Our model significantly enhances the accuracy of tumor evolution depiction, capturing detailed spatial and temporal changes within the tumor microenvironment. It is versatile and applicable to various downstream tasks, demonstrating strong generalizability across different spatial omics platforms, including 10× Visium and Visium HD and various cancer types, including colorectal cancer and prostate cancer. This robust performance improves research efficiency and provides valuable insights into tumor progression. Conclusions: SCOIGET offers an innovative solution by integrating multiple features and advanced algorithms, providing a detailed and accurate representation of tumor heterogeneity and evolution, aiding in the development of personalized cancer treatment strategies. Full article

With the exponential growth of digital music, efficiently identifying key music relationship nodes in large-scale music knowledge graphs is crucial for enhancing music recommendation, emotion analysis, and genre classification. To address this challenge, we propose MUSIGAIN, a GATv2-based adaptive framework that combines graph robustness metrics with advanced graph neural network mechanisms for multi-relationship mining in heterogeneous music knowledge graphs. MUSIGAIN tackles three fundamental challenges: the prohibitive computational complexity of exact graph-robustness calculations, the limitations of traditional centrality measures in capturing semantic heterogeneity, and the over-smoothing problem in deep graph neural networks. The framework introduces three key innovations: (1) a layer-wise dynamic skipping mechanism that adaptively controls propagation depth based on third-order embedding stability, reducing computation by 30–40% while preventing over-smoothing; (2) the DiGRAF adaptive activation function that enables node-specific nonlinear transformations to capture semantic heterogeneity across different entity types; and (3) ranking-based optimization supervised by graph robustness metrics, focusing on relative importance ordering rather than absolute value prediction. Experimental results on four real-world music knowledge graphs (POP-MKG, ROCK-MKG, JAZZ-MKG, CLASSICAL-MKG) demonstrate that MUSIGAIN consistently outperforms existing methods in Top-5% node identification accuracy, achieving up to 96.78% while maintaining linear scalability to graphs with hundreds of thousands of nodes. MUSIGAIN provides an efficient, accurate, and interpretable solution for key node identification in complex heterogeneous graphs. Full article